Our interest is to show the performance improvement of data-free compression, which is brought by the AutoReCon method. We adopt the GDFQ data-free compression method [1]} as a baseline for the following three reasons. First, it exploits both class boundary information and distribution information from the pre-trained model \(M_p\) , compared to other methods that use only one type of information [2]}, [3]}, [4]}, [5]}. Second, it includes both data-free quantization and data-free knowledge distillation, where knowledge distillation is applied for the output layer (i.e., knowledge distillation is not applied for the intermediate layers). Third, it achieves state-of-the-art performance. We use the same experimental settings as the GDFQ method to observe the influence of the generator architecture. In the GDFQ method, the human-designed generator architecture for both CIFAR-100 and ImageNet classification follows ACGAN. Besides, the human-designed generator for ImageNet classification adopts the categorical conditional batch normalization layer to fuse label information following SN-GAN.
